So much information about information

Information.  There is a lot of information about information.  Effective use of information is expected, yet in my experiences there are always “day after experts” that will tell you it could have been done better.

This is really the overarching question about information.  Craig Fugate has noted on a number of occasions: Every EOC has the weather radar or satellite image of a hurricane making landfall.  How many people in the EOC do you think can actually read that image?  Much of what you see in an EOC is eye-candy or part of the “theater of disaster” for visitors.

High-tech EOCs and pretty graphics are useless unless they help someone make a decision. – Craig Fugate

Information sources include: Historical records, personal experience, first responders, local and state EOCs, media (traditional & new), remote sensing, and the general public (inside and outside event area)

Imagine if all smart phones had sensors (like GPS, microphones, photo, video, and text capability) that people could use to upload multi-media information to first responders.

Oh wait.  They do.

Gathering and analyzing data

The challenge there is in the gathering and analyzing of the data.  That is where the information hierarchy or “DIKW” models come in to help understand it.

  • Data is a simple specific fact.  Alone it doesn’t do anything and doesn’t have much value.  Data needs to be built on.  Example: There are 1000 people in a shelter.
  • Information is data processed to be relevant and meaningful.  It adds the “what” element.  Example: There are 1000 people in a shelter and no toilets.
  • Knowledge is information combined with opinions, skills and experience.  It adds the “how to” element.  Example:  A 1000 people in a shelter will need 75 toilets.
  • Instead of Wisdom, I add Action.  Knowledge is good, but putting that knowledge to action is better.  Example: We need to get 75 toilets for the 1000 people in the shelter.

Imaging being asked three questions: “So what’s the problem?”; “What would fix it?”; “How will you do it?”  When you ask these questions, you are loosely following the DIKW model to turn data into action.

Information processing steps

All data systems, including you, will follow basic steps to process data.  These steps can be very simple or increasingly complex depending on the needs.

Project management folks will recognize a critical step that is assumed before these can occur: the requirements.  What are your information needs?  What is the goal that you’re trying to achieve with this work?

  • Collection: Gathering and Recording Information
  • Evaluation: Determine confidence: credibility, reliability, validity, relevance
  • Abstracting: Editing and reducing information
  • Indexing: Classification for retrieval
  • Storage: Physical storage of information
  • Dissemination: Get the right information to the right people at the right time

Many systems are good at the collection of data.  That’s usually the easy part.  Finding the right computer algorithms to handle evaluation to indexing is the hard part.  Google is an example of a company that has gotten this down so well that they became the top company for internet searches.

 Next: Databases, data sets and meta data


Additional resources